ロボットインタラクションを通じた触覚力推定のロバスト学習
Robust Learning of Tactile Force Estimation through Robot Interaction
SynTouch BioTac触覚センサの信号から力を推定するロバストなニューラルネットモデルを、3種類の方法で収集した14万サンプルで学習し、力の方向と大きさの推定精度を大幅に向上させた。
著者: Balakumar Sundaralingam, Alexander Lambert, Ankur Handa, Byron Boots, Tucker Hermans, Stan Birchfield, Nathan Ratliff, Dieter Fox
分類: cs.RO
原文アブストラクト
Current methods for estimating force from tactile sensor signals are either inaccurate analytic models or task-specific learned models. In this paper, we explore learning a robust model that maps tactile sensor signals to force. We specifically explore learning a mapping for the SynTouch BioTac sensor via neural networks. We propose a voxelized input feature layer for spatial signals and leverage information about the sensor surface to regularize the loss function. To learn a robust tactile force model that transfers across tasks, we generate ground truth data from three different sources: (1) the BioTac rigidly mounted to a force torque~(FT) sensor, (2) a robot interacting with a ball rigidly attached to the same FT sensor, and (3) through force inference on a planar pushing task by formalizing the mechanics as a system of particles and optimizing over the object motion. A total of 140k samples were collected from the three sources. We achieve a median angular accuracy of 3.5 degrees in predicting force direction (66% improvement over the current state of the art) and a median magnitude accuracy of 0.06 N (93% improvement) on a test dataset. Additionally, we evaluate the learned force model in a force feedback grasp controller performing object lifting and gentle placement. Our results can be found on https://sites.google.com/view/tactile-force.
